This function "cleans" names of model parameters by removing
patterns like "r_" or "b[]" (mostly applicable to Stan models)
and adding columns with information to which group or component parameters
belong (i.e. fixed or random, count or zero-inflated...)
The main purpose of this function is to easily filter and select model parameters,
in particular of - but not limited to - posterior samples from Stan models,
depending on certain characteristics. This might be useful when only selective
results should be reported or results from all parameters should be filtered
to return only certain results (see print_parameters()).
Value
A data frame with "cleaned" parameter names and information on effects,
component and group where parameters belong to. To be consistent across
different models, the returned data frame always has at least four columns
Parameter, Effects, Component and Cleaned_Parameter. See 'Details'.
Details
The Effects column indicate if a parameter is a fixed or random effect.
The Component can either be conditional or zero_inflated. For models
with random effects, the Group column indicates the grouping factor of the
random effects. For multivariate response models from brms or
rstanarm, an additional Response column is included, to indicate
which parameters belong to which response formula. Furthermore,
Cleaned_Parameter column is returned that contains "human readable"
parameter names (which are mostly identical to Parameter, except for for
models from brms or rstanarm, or for specific terms like smooth-
or spline-terms).
Examples
# \donttest{
model <- download_model("brms_zi_2")
clean_parameters(model)
#> Parameter Effects Component Group
#> 1 b_Intercept fixed conditional
#> 2 b_persons fixed conditional
#> 3 b_child fixed conditional
#> 4 b_camper fixed conditional
#> 5 r_persons[1,Intercept] random conditional Intercept: persons
#> 6 r_persons[2,Intercept] random conditional Intercept: persons
#> 7 r_persons[3,Intercept] random conditional Intercept: persons
#> 8 r_persons[4,Intercept] random conditional Intercept: persons
#> 9 sd_persons__Intercept random conditional SD/Cor: persons
#> 10 b_zi_Intercept fixed zero_inflated
#> 11 b_zi_child fixed zero_inflated
#> 12 b_zi_camper fixed zero_inflated
#> 13 r_persons__zi[1,Intercept] random zero_inflated Intercept: persons
#> 14 r_persons__zi[2,Intercept] random zero_inflated Intercept: persons
#> 15 r_persons__zi[3,Intercept] random zero_inflated Intercept: persons
#> 16 r_persons__zi[4,Intercept] random zero_inflated Intercept: persons
#> 17 sd_persons__zi_Intercept random zero_inflated SD/Cor: persons
#> Cleaned_Parameter
#> 1 (Intercept)
#> 2 persons
#> 3 child
#> 4 camper
#> 5 persons.1
#> 6 persons.2
#> 7 persons.3
#> 8 persons.4
#> 9 (Intercept)
#> 10 (Intercept)
#> 11 child
#> 12 camper
#> 13 persons.1
#> 14 persons.2
#> 15 persons.3
#> 16 persons.4
#> 17 (Intercept)
# }